Foundation Model Hidden Representations for Heart Rate Estimation from Auscultation

• URL: http://arxiv.org/abs/2505.20745v2
• Date: Thu, 29 May 2025 17:51:17 GMT
• Title: Foundation Model Hidden Representations for Heart Rate Estimation from Auscultation
• Authors: Jingping Nie, Dung T. Tran, Karan Thakkar, Vasudha Kowtha, Jon Huang, Carlos Avendano, Erdrin Azemi, Vikramjit Mitra,
• Abstract summary: Auscultation, particularly heart sound, is a non-invasive technique that provides essential vital sign information.<n>Recently, self-supervised acoustic representation foundation models (FMs) have been proposed to offer insights into acoustics-based vital signs.
• Score: 3.1379239557375223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Auscultation, particularly heart sound, is a non-invasive technique that provides essential vital sign information. Recently, self-supervised acoustic representation foundation models (FMs) have been proposed to offer insights into acoustics-based vital signs. However, there has been little exploration of the extent to which auscultation is encoded in these pre-trained FM representations. In this work, using a publicly available phonocardiogram (PCG) dataset and a heart rate (HR) estimation model, we conduct a layer-wise investigation of six acoustic representation FMs: HuBERT, wav2vec2, wavLM, Whisper, Contrastive Language-Audio Pretraining (CLAP), and an in-house CLAP model. Additionally, we implement the baseline method from Nie et al., 2024 (which relies on acoustic features) and show that overall, representation vectors from pre-trained foundation models (FMs) offer comparable performance to the baseline. Notably, HR estimation using the representations from the audio encoder of the in-house CLAP model outperforms the results obtained from the baseline, achieving a lower mean absolute error (MAE) across various train/validation/test splits despite the domain mismatch.

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